Shifting technological landscapes have been shaping the epistemologies, methods, and workflows of classicists, historians, and archaeologists for more than a century. Yet these technologies and their impacts have not always been subject to reflexive interrogation within our disciplines. As artificial intelligence (AI) technologies – especially generative AI – have become widely accessible, there has been a wave of concern among educators around student learning outcomes and academic honesty. In addressing these challenges, we must also engage in critical reflection around the use of AI technologies in our research and teaching from humanist and social scientist perspectives in order to serve critical learning outcomes and counter rising digital colonialism. This paper first situates the conversation within the context of corporate AI economies and the intersections between AI technologies and antiquity-focused fields. It then proposes three AI-focused literacies for cultivating core ancient Mediterranean studies competencies and fostering critical thinking: the ability to identify and analyse the impact of taxonomic frameworks and data training inputs on algorithmic outputs; the ability to understand the development of computational models and algorithmic reasoning and their applications; and the ability to critically evaluate the ethical and environmental impacts of algorithmic reasoning and AI technologies. Critical assessment of the foundational structures and logics of AI systems and economies situates both educators and learners to critically assess the political, social, and environmental impacts of such systems on the study of the past and the world in the present.

Recently, an epistemic shift has occurred in classics pedagogy. First, the COVID pandemic forced students online. Then, large language model (LLM) chatbots appeared. These disruptions created a conundrum: how could research methods, reading and writing, and cross-cultural exploration be taught when students were tethered to LLM-accessible ‘cheating machines’? The solution was AI-assisted classics. This paper describes how AI-assisted classical history can be taught through a discussion of the ‘WHAM’ (Web Design, HTML, and the Archaeology of the Mediterranean) project. WHAM is a website-building classroom assignment developed to teach students in the Computer Science and Society major at Wentworth Institute of Technology (Boston, MA, USA; www.wit.edu) how to utilize digital tools to explicate humanities data. Students create a website from the HTML code up focused on a Mediterranean archaeological region and are taught to use LLMs critically, both for writing HTML code and for ideating web design features. In the process, students learn how LLMs must be approached cautiously since they can both hallucinate and offer satisfactory work that should be improved. Finally, the paper suggests that LLMs can provide students with a starting place for digital projects that challenge them to engage with the classical world’s history and material culture.

RUCAI is an AI-based open-source software platform designed for humanities and social sciences educational workflows that require traceability to assigned course materials. RUCAI has a teacher-only workspace in which teachers can curate class exercises grounded in curriculum documents (articles, book chapters, and notes). From here, the teacher can set up workspaces grounded explicitly in curriculum materials, learning outcomes, and course requirements for students to use throughout the course. The system is set up to run on institution-controlled infrastructure using FastAPI, PostgreSQL, and a local LLM model serving via Ollama, and is intended to be deployable on a GPU-enabled virtual machine. RUCAI’s primary contributions are practical: (1) it presents a software architecture for local-first, source-grounded educational AI that treats the course as the primary software object; (2) it documents concrete implementation choices for reliable ingestion and retrieval across heterogeneous course materials, including a three-tier extraction pipeline, word-based chunking, and intent-aware retrieval; and (3) it describes a publish-to-student mechanism that lets teachers freeze course context and prompt constraints into student-facing instances. The paper also reports pilot observations, current limitations, and future development directions.

Colonial Korean printshops (1910–1945) operated within severe material constraints that produced apparent typographic standardization. Manual bibliography has nevertheless identified subtle shop-specific signatures in letterforms. This article tests whether computational methods can detect these signatures systematically across large corpora. We apply multiple instance learning to 57,583 page images from four major printshops in Kyŏngsŏng (Seoul). To avoid same-book leakage, we evaluate page classification with all pages from each tested book held out from training. Under this stricter protocol, the model assigns pages to printshops with 84.5 percent accuracy while producing interpretable attention maps. Clustering high-attention patches reveals that the four presses occupy the same regions of the embedding space, reflecting their shared typographic infrastructure. Differentiation emerges through density patterns within this shared space rather than through distinct typographic vocabularies. Each press concentrates disproportionately in clusters capturing specific sub-character features, such as terminal angles, serif weight, and stroke thickness. These computationally identified patterns align with features that De Fremery and Ryu documented through intensive manual analysis of individual volumes. The correspondence supports the approach while extending it to scales that manual bibliography cannot achieve. This framework shows how computational analysis can test and extend humanities scholarship on historical print culture by surfacing the specific visual features that distinguish institutional outputs within materially constrained production systems.

This comment examines the rapidly evolving ecosystem of historical research data in the United Kingdom, where cultural heritage collections are increasingly digitised, commercialised and fragmented. Historians face growing challenges in discovering, accessing and reusing data as resources move behind paywalls, and repositories remain scattered, without a national infrastructure to ensure long-term preservation or discoverability. Drawing on examples from major digital initiatives, we analyse the life cycle of historical research data and highlight the complex interplay of commercial, institutional and scholarly interests that shape access. We distinguish three types of data that emerge from historians’ typical engagements with digitised collections: derived, enhanced and aggregated data. We argue that historians must actively participate in the practices relating to the creation, maintenance and reuse of such data. This will involve new forms of citation, favouring open datasets, improving digital skills and building communities around shared resources. The comment concludes with proposals to improve discoverability, sustainability and reuse, urging the discipline to establish common standards and infrastructures to secure an equitable data commons for future research.

The ending speech in Aesopic fables, where stories conclude with direct utterances from characters, is not merely a didactic tool but a crucial narrative device constructing hermeneutic complexity. This study systematically examines the narrative function of ending speech through computational analysis of 600 Aesopic fables from Laura Gibbs’ edition. We quantitatively analyzed the complex relationships between ending speech, story content, explicit morals and speaker identity using natural language processing techniques. The analysis reveals three key findings. First, the average similarity of ending speeches (0.1820) is significantly lower than that of stories (0.3578), confirming that ending speech forms a unique semantic domain rather than serving as a simple summary of the narrative. Latent Dirichlet allocation analysis also shows that ending speeches are differentiated into 13 topics, displaying a more complex structure than stories (seven topics). Second, we found that ending speech constitutes a distinct narrative domain from epimythium, with an overwhelming ratio of their relationships being either independent (76.8%) or tensional (21.4%). This indicates that the ending speech is a narrative device that amplifies interpretive complexity, often clashing with the epimythium rather than reinforcing it. Third, 249 different ending speech speakers each represent unique voices and perspectives, with the frequency of utterances – fox (34 times), lion (19 times) and wolf (18 times) – demonstrating a value system in Aesopic fables where wisdom is prioritized over physical strength. These findings indicate that the ending speech establishes complex and sometimes tensional relationships with both story and epimythium, thereby transforming fables into “open work” that can be newly interpreted. This study provides empirical evidence for understanding Aesopic fables not as simple didactic tales but as complex narratives with structural features supporting polyphonic interpretation, demonstrating the potential of computational narratology.

Large-language models (LLMs) have transformed natural language processing and opened new possibilities for the computational social sciences and digital humanities. Yet translating historical sources remains difficult because early modern varieties are scarcely represented in contemporary training corpora and because standard tokenizers fragment their non-standard orthography. This article tackles these gaps by adapting open LLMs to early modern Dutch-to-English translation and advances two concrete contributions: (i) a memory-efficient fine-tuning workflow that runs on a single consumer GPU, comparing order-reward policy optimization with the Unsloth supervised fine-tuning approach and (ii) a verifiable evaluation protocol that combines embedding-based metrics with systematic expert review. Experiments on testimonial texts (1680–1792) show that fine-tuning choice decisively shapes quality: the Unsloth-tuned Mistral model attains the highest BERTScore and METEOR values and most faithfully preserves historical nuance. The framework supports a collaborative workflow where machine-generated drafts accelerate expert translation, making archival texts more accessible while maintaining scholarly oversight through domain-expert validation.

This introductory article to Democratic Theory's special issue on the marginalized democracies of the world begins by presenting the lexical method for understanding democracy. It is argued that the lexical method is better than the normative and analytical methods at finding democracies in the world. The argument then turns to demonstrating, mainly through computational research conducted within the Google Books catalog, that an empirically demonstrable imbalance exists between the democracies mentioned in the literature. The remainder of the argument is given to explaining the value of working to correct this imbalance, which comes in at least three guises: (1) studying marginalized democracies can increase our options for alternative democratic actions and democratic innovations; (2) it leads to a conservation and public outreach project, which is epitomized in an “encyclopedia of the democracies”; and (3) it advocates for a decolonization of democracies’ definitions and practices and decentering academic democratic theory.

The volumes of historical data locked behind unstructured formats have long been a challenge for researchers in the computational humanities. While optical character recognition (OCR) and natural language processing have enabled large-scale text mining projects, the irregular formatting, inconsistent terminology and evolving printing practices complicate automated parsing and information extraction efforts for historical documents. This study explores the potential of large language models (LLMs) in processing and structuring irregular and non-standardized historical materials, using the U.S. Department of Agriculture’s Plant Inventory books (1898–2008) as a test case. Given the frequent evolution of these historical records, we implemented a pipeline combining OCR, custom segmentation rules and LLMs to extract structured data from the scanned texts. It provides an example of how incorporating LLMs into data-processing pipelines can enhance the accessibility and usability of historical and archival materials for scholars.